SeLeP: Learning Based Semantic Prefetching for Exploratory Database Workloads
Summary: SeLeP: semantic prefetching for exploratory SQL by encoding block values and framing prefetching as a time-series forecasting problem. An encoder–decoder LSTM learns semantic (not address) access patterns, improving hit ratio up to 40% and cutting I/O ~45% (96% hit, 84% I/O reduction avg). (summarized by gpt-5-mini on Feb 09 2026)
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